dantro.dag module

This is an implementation of a DAG for transformations on dantro objects

class dantro.dag.Transformation(*, operation: str, args: Sequence[Union[dantro._dag_utils.DAGReference, Any]], kwargs: Dict[str, Union[dantro._dag_utils.DAGReference, Any]], dag: Optional[dantro.dag.TransformationDAG] = None, salt: int = None, file_cache: dict = None)

Bases: object

A transformation is the collection of an N-ary operation and its inputs.

Transformation objects store the name of the operation that is to be carried out and the arguments that are to be fed to that operation. After a Transformation is defined, the only interaction with them is via the compute method.

For computation, the arguments are recursively inspected for whether there are any DAGReference-derived objects; these need to be resolved first, meaning they are looked up in the DAG’s object database and – if they are another Transformation object – their result is computed. This can lead to a traversal along the DAG.

Warning

Objects of this class should under no circumstances be changed after they were created! For performance reasons, the hashstr property is cached; thus, changing attributes that are included into the hash computation will not lead to a new hash, thus silently creating wrong behaviour.

All relevant attributes (operation, args, kwargs, salt) are thus set read-only. This should be respected!

__init__(*, operation: str, args: Sequence[Union[dantro._dag_utils.DAGReference, Any]], kwargs: Dict[str, Union[dantro._dag_utils.DAGReference, Any]], dag: Optional[dantro.dag.TransformationDAG] = None, salt: int = None, file_cache: dict = None)

Initialize a Transformation object.

Parameters

• operation (str) – The operation that is to be carried out.

• args (Sequence[Union[DAGReference, Any]]) – Positional arguments for the operation.

• kwargs (Dict[str, Union[DAGReference, Any]]) – Keyword arguments for the operation. These are internally stored as a KeyOrderedDict.

• dag (TransformationDAG, optional) – An associated DAG that is needed for object lookup. Without an associated DAG, args or kwargs may NOT contain any object references.

• salt (int, optional) – A hashing salt that can be used to let this specific Transformation object have a different hash than other objects, thus leading to cache misses.

• file_cache (dict, optional) –

  File cache options. Expected keys are write (boolean or dict) and read (boolean or dict). Note that the options given here are NOT reflected in the hash of the object!

  The following arguments are possible under the read key:

  enabled (bool, optional):

  Whether it should be attempted to read from the file cache.

  load_options (dict, optional):

  Passed on to the method that loads the cache, load().

  Under the write key, the following arguments are possible. They are evaluated in the order that they are listed here. See _cache_result() for more information.

  enabled (bool, optional):

  Whether writing is enabled at all

  always (bool, optional):

  If given, will always write.

  allow_overwrite (bool, optional):

  If False, will not write a cache file if one already exists. If True, a cache file might be written, although one already exists. This is still conditional on the evaluation of the other arguments.

  min_size (int, optional):

  The minimum size of the result object that allows writing the cache.

  max_size (int, optional):

  The maximum size of the result object that allows writing the cache.

  min_compute_time (float, optional):

  The minimal individual computation time of this node that is needed in order for the file cache to be written. Note that this value can be lower if the node result is not computed but looked up from the cache.

  min_cumulative_compute_time (float, optional):

  The minimal cumulative computation time of this node and all its dependencies that is needed in order for the file cache to be written. Note that this value can be lower if the node result is not computed but looked up from the cache.

  storage_options (dict, optional):

  Passed on to the cache storage method, dantro.dag.TransformationDAG._write_to_cache_file(). The following arguments are available:

  ignore_groups (bool, optional):

  Whether to store groups. Disabled by default.

  attempt_pickling (bool, optional):

  Whether it should be attempted to store results that could not be stored via a dedicated storage function by pickling them. Enabled by default.

  raise_on_error (bool, optional):

  Whether to raise on error to store a result. Disabled by default; it is useful to enable this when debugging.

  pkl_kwargs (dict, optional):

  Arguments passed on to the pickle.dump function.

  further keyword arguments:

  Passed on to the chosen storage method.

__str__() → str

A human-readable string characterizing this Transformation

__repr__() → str

A deterministic string representation of this transformation.

Note

This is also used for hash creation, thus it does not include the attributes that are set via the initialization arguments dag and file_cache.

Warning

Changing this method will lead to cache invalidations!

property hashstr

Computes the hash of this Transformation by creating a deterministic representation of this Transformation using __repr__ and then applying a checksum hash function to it.

Note that this does NOT rely on the built-in hash function but on the custom dantro _hash function which produces a platform-independent and deterministic hash. As this is a string-based (rather than an integer-based) hash, it is not implemented as the __hash__ magic method but as this separate property.

Returns

The hash string for this transformation

Return type

str

__hash__() → int

Computes the python-compatible integer hash of this object from the string-based hash of this Transformation.

property dag

The associated TransformationDAG; used for object lookup

property dependencies

Recursively collects the references that are found in the positional and keyword arguments of this Transformation.

property resolved_dependencies

Transformation objects that this Transformation depends on

property profile

The profiling data for this transformation

yaml_tag = '!dag_trf'

classmethod from_yaml(constructor, node)

classmethod to_yaml(representer, node)

A YAML representation of this Transformation, including all its arguments (which must again be YAML-representable). In essence, this returns a YAML mapping that has the !dag_trf YAML tag prefixed, such that reading it in will lead to the from_yaml method being invoked.

Note

The YAML representation does not include the file_cache parameters.

Warning

The YAML representation is used in computing the hashstr that identifies this transformation. Changing the argument order here or adding further keys to the dict will lead to hash changes and thus to cache misses.

compute() → Any

Computes the result of this transformation by recursively resolving objects and carrying out operations.

This method can also be called if the result is already computed; this will lead only to a cache-lookup, not a re-computation.

Returns

The result of the operation

Return type

Any

_perform_operation(*, args, kwargs) → Any

Perform the operation, updating the profiling info on the side

_update_profile(*, cumulative_compute: float = None, **times) → None

Given some new profiling times, updates the profiling information.

Parameters

• cumulative_compute (float, optional) – The cumulative computation time; if given, additionally computes the computation time for this individual node.

• **times – Valid profiling data.

_lookup_result() → Tuple[bool, Any]

Look up the transformation result to spare re-computation

_cache_result(result: Any) → None

Stores a computed result in the cache

class dantro.dag.TransformationDAG(*, dm: DataManager, select: dict = None, transform: Sequence[dict] = None, cache_dir: str = '.cache', file_cache_defaults: dict = None, base_transform: Sequence[dantro.dag.Transformation] = None, select_base: Union[dantro._dag_utils.DAGReference, str] = None, select_path_prefix: str = None)

Bases: object

This class collects transformation operations that are (already by their own structure) connected into a directed acyclic graph. The aim of this class is to maintain base objects, manage references, and allow operations on the DAG, the most central of which is computing the result of a node.

Furthermore, this class also implements caching of transformations, such that operations that take very long can be stored (in memory or on disk) to speed up future operations.

Objects of this class are initialized with dict-like arguments which specify the transformation operations. There are some shorthands that allow a simple definition syntax, for example the select syntax, which takes care of selecting a basic set of data from the associated DataManager.

__init__(*, dm: DataManager, select: dict = None, transform: Sequence[dict] = None, cache_dir: str = '.cache', file_cache_defaults: dict = None, base_transform: Sequence[dantro.dag.Transformation] = None, select_base: Union[dantro._dag_utils.DAGReference, str] = None, select_path_prefix: str = None)

Initialize a DAG which is associated with a DataManager and load the specified transformations configuration into it.

Parameters

• dm (DataManager) – The associated data manager

• select (dict, optional) – Selection specifications, which are translated into regular transformations based on getitem operations. The base_transform and select_base arguments can be used to define from which object to select. By default, selection happens from the associated DataManager.

• transform (Sequence[dict], optional) – Transform specifications.

• cache_dir (str, optional) – The name of the cache directory to create if file caching is enabled. If this is a relative path, it is interpreted relative to the associated data manager’s data directory. If it is absolute, the absolute path is used. The directory is only created if it is needed.

• file_cache_defaults (dict, optional) – Default arguments for file caching behaviour. This is recursively updated with the arguments given in each individual select or transform specification.

• base_transform (Sequence[Transformation], optional) – A sequence of transform specifications that are added to the DAG prior to those added via select and transform. These can be used to create some other object from the data manager which should be used as the basis of select operations.

• select_base (Union[DAGReference, str], optional) – Which tag to base the select operations on. If None, will use the (always-registered) tag for the data manager, dm. This attribute can also be set via the select_base property.

• select_path_prefix (str, optional) – If given, this path is prefixed to all path specifications made within the select argument. Note that unlike setting the select_base this merely joins the given prefix to the given paths, thus leading to repeated path resolution. For that reason, using the select_base argument is generally preferred and the select_path_prefix should only be used if select_base is already in use. If this path ends with a /, it is directly prepended. If not, the / is added before adjoining it to the other path.

__str__() → str

A human-readable string characterizing this TransformationDAG

property dm

The associated DataManager

property hashstr

Returns the hash of this DAG, which depends solely on the hash of the associated DataManager.

property objects

The object database

property tags

A mapping from tags to objects’ hashes; the hashes can be looked up in the object database to get to the objects.

property nodes

The nodes of the DAG

property cache_dir

The path to the cache directory that is associated with the DataManager that is coupled to this DAG. Note that the directory might not exist yet!

property cache_files

Scans the cache directory for cache files and returns a dict that has as keys the hash strings and as values a tuple of full path and file extension.

property select_base

The reference to the object that is used for select operations

property profile

Returns the profiling information for the DAG.

property profile_extended

Builds an extended profile that includes the profiles from all transformations and some aggregated information.

This is calculated anew upon each invocation; the result is not cached.

add_node(*, operation: str, args: list = None, kwargs: dict = None, tag: str = None, file_cache: dict = None, **trf_kwargs) → dantro._dag_utils.DAGReference

Add a new node by creating a new Transformation object and adding it to the node list.

Parameters

• operation (str) – The name of the operation

• args (list, optional) – Positional arguments to the operation

• kwargs (dict, optional) – Keyword arguments to the operation

• tag (str, optional) – The tag the transformation should be made available as.

• file_cache (dict, optional) – File cache options for this node. If defaults were given during initialization, those defaults will be updated with the given dict.

• **trf_kwargs – Passed on to Transformation.__init__

Raises

ValueError – If the tag already exists

Returns

The reference to the created node

Return type

DAGReference

add_nodes(*, select: dict = None, transform: Sequence[dict] = None)

Adds multiple nodes by parsing the specification given via the select and transform arguments.

Parameters

• select (dict, optional) – Selection specifications, which are translated into regular transformations based on getitem operations. The base_transform and select_base arguments can be used to define from which object to select. By default, selection happens from the associated DataManager.

• transform (Sequence[dict], optional) – Transform specifications.

compute(*, compute_only: Sequence[str] = None) → Dict[str, Any]

Computes all specified tags and returns a result dict.

Parameters

• compute_only (Sequence[str], optional) – The tags to compute. If not given, will compute all associated tags.

• cache_options (dict, optional) – Cache options. These will update the default cache options given at initialization of the DAG.

Returns

A mapping from tags to fully computed results.

Return type

Dict[str, Any]

_parse_trfs(*, select: dict, transform: Sequence[dict]) → Sequence[dict]

Parse the given arguments to bring them into a uniform format: a sequence of parameters for transformation operations.

Parameters

• select (dict) – The shorthand to select certain objects from the DataManager. These may also include transformations.

• transform (Sequence[dict]) – Actual transformation operations, carried out afterwards.

Returns

A sequence of transformation parameters that was brought into a uniform structure.

Return type

Sequence[dict]

Raises

• TypeError – On invalid type within entry of select

• ValueError – When file_cache is given for selection from base

_update_profile(**times)

Updates profiling information by adding the given time to the matching key.

_retrieve_from_cache_file(trf_hash: str, **load_kwargs) → Tuple[bool, Any]

Retrieves a transformation’s result from a cache file.

_write_to_cache_file(trf_hash: str, *, result: Any, ignore_groups: bool = True, attempt_pickling: bool = True, raise_on_error: bool = False, pkl_kwargs: dict = None, **save_kwargs) → bool

Writes the given result object to a hash file, overwriting existing ones.

Parameters

• trf_hash (str) – The hash; will be used for the file name

• result (Any) – The result object to write as a cache file

• ignore_groups (bool, optional) – Whether to store groups. Disabled by default.

• attempt_pickling (bool, optional) – Whether it should be attempted to store results that could not be stored via a dedicated storage function by pickling them. Enabled by default.

• raise_on_error (bool, optional) – Whether to raise on error to store a result. Disabled by default; it is useful to enable this when debugging.

• pkl_kwargs (dict, optional) – Arguments passed on to the pickle.dump function.

• **save_kwargs – Passed on to the chosen storage method.

Returns

Whether a cache file was saved

Return type

bool

Raises

• NotImplementedError – When attempting to store instances of BaseDataGroup or a derived class

• RuntimeError – When raise_on_error was given and there was an error during saving.